TI 2013-115/VIII Tinbergen Institute Discussion Paper
Dynamics in Aceh and North Sumatera after the Twin Disasters
Aloysius Gunadi Brataa,b Henri L.F. de Groota,c,d Piet Rietvelda,c
a Faculty of Economics and Business Administration, VU University Amsterdam, The Netherlands; b Atma Jaya Yogyakarta University, Department of Economics, Yogyakarta, Indonesia; c Tinbergen Institute, Amsterdam, The Netherlands; d Ecorys NEI, Rotterdam, The Netherlands.
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Dynamics in Aceh and North Sumatera after the Twin Disasters
An investigation into the relevance of the locational fundamental theory
Aloysius Gunadi Brataa,b, Henri L.F. de Groota,c,d and Piet Rietvelda,c
a VU University Amsterdam, Department of Spatial Economics, The Netherlands b Atma Jaya Yogyakarta University, Department of Economics, Indonesia c Tinbergen Institute, Amsterdam, The Netherlands d Ecorys NEI, Rotterdam, The Netherlands
Abstract This paper analyzes the impact of the Indian Ocean Tsunami in 2004 and the Nias earthquake in 2005 on population dynamic across regions in Aceh and North Sumatera. We find no evidence that the disasters affected the regularity of size-distribution of the regions. The estimates of a population growth model yield clear evidence that the disasters had only a temporary impact. This study provides indicative evidence for the relevance of the locational fundamental theory and rejects the random growth explanation.
Keywords : natural disaster; population growth; rank-size distribution JEL code : R11, R12, Q54
“A city is hard to kill, in part because of its strategic geographical location, its concentrated, persisting stock of physical capital, and even more because of the memories, motives, and skill of its inhabitants.” (Kevin Lynch, 1972: 3-4, cited by Vale and Campanella 2005: 347)
1. Introduction There are several stylized facts with regard to the growth of cities across countries. Cities in many countries are growing according to a regular pattern, both in terms of their size (city population) and their rank in a system of cities. The regularity over decades or centuries means that even large shocks did not affect the distribution of cities permanently. This regularity became a fascinating ‘law’ for economists to evaluate and to explain theoretically (see, for instance, Gabaix and Ioannides, 2004; Krugman, 1996; Nitsch, 2005). Another dimension concerns the location of cities. Vale and Campanella (2005) conclude that one of the city resilience axioms is the power of place which contributes to city resilience after it was destructed by a major disaster. They derived this from studies on the resilience of cities in different time periods, places and cultures. Although this conclusion is based on a limited number of studies on individual cities that have been destroyed by large shocks, it provides evidence that cities which have locational advantages will rebound and subsequently keep their high rank in the distribution of cities. In the same spirit, Davis and Weinstein (2002), Brakman et al. (2004) and Bosker et al. (2008) used the bombing of Japan and German during the second World War (WW-II) as “quasi natural experiments” and found that the impact of shocks on the city-size distribution is only temporary and supports the locational fundamental theory. Investigating another post WW-II “natural experiment” – the bombing of Vietnam by the US – Miguel and Roland (2011) did not find strong evidence of permanent effects from this bombing on local poverty rates, consumption levels, infrastructure, literacy, or population density. They used a different approach, but the main conclusion is parallel to Japan and German cases, which is that the bombing affected Vietnam only temporary. Basically, locational fundamentals theory states that natural advantage or the physical landscape (first nature advantages such as access to the sea, river, or availability of natural
1 resources), as initial conditions, determines the existence as well as the growth of cities (see, for instance, Fujita and Mori, 1996; Krugman, 1996). These permanent features give benefits to the locations as an excellent site for economic activity (Davis and Weinstein, 2002) and as such have affected the formation and evolution of the size of a location. It is oftentimes argued that in normal situations, the importance of such initial conditions should become smaller, but their impacts may still persist, for instance through cumulative process by which first nature advantages create second nature advantages. Against this background, it is interesting to see how the growth and the position of a city in the rank size distribution is affected by extreme events: do the initial locational advantages play an important role in reverting the growth of the city after the shock? Following Davis and Weinstein (2002), the main aim of this paper is to investigate the relevance of the locational fundamental theory in a very specific situation, viz. the cross- regions in Northern Sumatera (Aceh and North Sumatera province) after the Indian Ocean tsunami and the Nias earthquake. Since most empirical studies on urban growth focus on the developed countries, taking Northern Sumatera as a “natural laboratory” will contribute to recent empirical studies on developing countries such as for instance China (Anderson and Ge, 2005; Chen et al., 2010), Malaysia (Soo, 2007), or on growth of sub-national (state) population in India, China and Brazil (Soo, 2010). Another important issue in studying this size distribution to which we aim to contribute is the appropriate urban-regional unit of observation. In their recent papers, Giesen and Suedekum (2011) and González-Val and Sanso-Navarro (2010) argue that Zipf’s law can also hold within regions of a country. Applying three different concepts of regions in Germany (random regions, the German Federal States, and a spatial club of cities), Giesen and Suedekum show that city size distributions at national and regional levels tend to follow a power law. A similar message on this issue also emerged when regions in the USA were clustered as “Megapolitan Areas” (see Berry and Okulicz-Kozaryn, 2011). These studies argue in favour of using data for single regions. This may be particularly relevant for regions such as Aceh and Nias in large developing countries such Indonesia. Based on our empirical work, we conclude there are no significant changes of relative positions of regions in Aceh and North Sumatera, despite the fact that they have been hit by big disasters. Post- and pre-disaster ranks are highly correlated as well as population size.
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Furthermore, the twin disasters did not affect the regularity of the size distribution of regions in Aceh and North Sumatera. The estimated population growth model yields clear evidence that the twin disasters had only a temporary impact. Overall, this study provides indicative evidence that the locational fundamental theory holds. The rest of this paper is structured as follows. The next section provides some relevant background. In Section 3, we discuss our framework to investigate the impact of the twin disasters on population dynamics. We proceed with results and discussion in Section 4. Secction 5 concludes.
2. Setting the Scene The Indian Ocean tsunami occurred after a massive earthquake with 9.3 magnitudes (on the Richter scale) of the west coast of Sumatra on December 26, 2004. The Nias earthquake occurred a couple of months later on March 28, 2005 (see Figure 1). There is no doubt that these natural disasters meet the basic criteria for ‘natural experiments’ in that they are exogenous, large, variable, and purely temporary (cf. Davis and Weinstein, 2008). More than 300,000 people were killed by the giant tsunami which also resulted in huge physical destruction. Meanwhile, the Nias earthquake with 8.7 magnitudes on the Richter scale killed more than 900 people, and strongly affected the Nias island. In response to these natural disasters, the Government of Indonesia (GoI) established the Agency for the Rehabilitation and Reconstruction of Aceh and Nias.1 The operation of this agency is regarded as one of the largest humanitarian programmes in history with nearly 500 participating actors (see Takahashi et al., 2007; Masyarafah and McKeon, 2008).
1 This agency, which is locally known as BRR (Badan Rehabilitasi dan Rekonstruksi), was established in 2005 and was operational for four years, based in Banda Aceh with a regional office in Nias and a representative office in Jakarta. It coordinated and jointly implemented a community-driven recovery program for Aceh and Nias. See http://kc.monevacehnias.bappenas.go.id/Modules/Home-Accordion/about-brr.html.
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Figure 1. Epicentres of the twin disasters
Source: Nazara and Resosudarmo (2007), with permission. We modified the map by adding the yellow and blue boxes.
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Table 1. The impact of the twin disasters in Aceh and Nias Internally Displaced House Conflict No Region Deaths and missing People Damaged Intensity per 1000 per 1000 Index total people* total people* % 1 Aceh Barat 14854 86.00 67817 392.63 45 1.49 2 Aceh Barat Daya 9 0.08 3480 30.13 13 0.56 3 Aceh Besar 47784 161.30 97466 329.00 32 0.74 4 Aceh Jaya 19804 248.39 40422 507.00 61 1.75 5 Aceh Selatan 9 0.05 19049 97.87 10 1.56 6 Aceh Singkil 50 0.37 30967 226.84 6 0.80 7 Aceh Tamiang 4 0.02 3396 14.75 1 0.69 8 Aceh Tengah 209 0.78 6107 22.65 2 1.90 9 Aceh Tenggara 172 1.08 809 5.06 2 0.87 10 Aceh Timur 224 0.70 14411 45.30 1 3.63 11 Aceh Utara 2726 5.36 32246 63.39 8 1.64 12 Bireuen 1261 3.54 42143 118.15 9 1.04 13 Gayo Lues 3 0.04 0 0.00 1 1.50 14 Nagan Raya 1338 10.00 17040 127.29 9 2.11 15 Pidie 6109 12.32 81532 164.37 8 1.65 16 Simeulue 45 0.65 42751 621.91 45 0.22 17 Banda Aceh 77804 387.37 50970 253.77 59 0.00 18 Langsa 0 0.00 3489 26.79 0 0.00 19 Lhokseumawe 404 2.51 7577 47.06 6 1.35 20 Sabang 14 0.53 3712 139.82 6 0.00 21 Nias 967 1.37 70000 99.25 79 0.00 Mean of sample (threshold value) 4456 23.65 16292 85.46 10 0.60
Sources of data: see Appendix. * Population data are an average of the population in 2003 and 2005. Regions in North Sumatera that were not affected by the twin disaster were excluded from this table.
Table 1 provides figures of the impact of the disasters on the population of Aceh and Nias. We do not provide data for other regions in North Sumatera since they were not affected, but we include them as control regions in our empirical analysis. It is important to note that regions in this table have been aggregated using 2003 as the reference point in order to avoid inconsistencies in the data caused by changes in administrative boundaries related to the decentralization. One of the impacts of decentralization in Indonesia is proliferation of
5 administrative regions across the country (cf. Fitrani et al. 2005). As a result, the number of regions in Aceh and North Sumatera has been increasing. We selected 2003 as the reference point by considering that the number of regions – especially in Aceh before 2003 – is quite small. Regencies or cities created since 2003 have been merged with the parent regions in our dataset.2 Using this procedure, Aceh in our dataset consists of 16 regencies and four cities while North Sumatera has 13 regencies and six cities. Thus, there are 29 regencies and 10 cities in our sample. Overall the table reveals the magnitude of the disasters in terms of the impact on human beings as well as on houses. For the purpose of our study, we classified the regions into two groups: (1) highly affected regions and (2) non- or limitedly affected regions. We adopted a method that has been used by Cavallo et al. (2010) to identify large international disasters during the period 1970–2008. The threshold values for classifying the regions are the means of the density of disaster impacts (number of deaths and missing per 1000 people, number of Internally Displaced People per 1000 people and percentage of houses damaged). A region is classified as a highly affected region if at least one of these three indicators is greater than the relevant threshold. Based on this procedure, 13 regions, mostly in the western and southern coastal areas of Aceh and the Nias island of North Sumatera, were categorized as highly affected regions (bold numbers in Table 1 and yellow boxes in Figure 1). Regarding population dynamics, it is known that compared to other provinces in Indonesia, the population growth rate in Aceh has fallen drastically especially between 2000 and 2005 (BPS-Statistics et al., 2010, pp. 16–17). The three key factors explaining this drastic fall are (i) higher mortality due to the tsunami, earthquakes and civil war, (ii) a falling birth rate, and (iii) outmigration. Besides increased mortality, the military conflict also contributed to a falling birth rate. Both the conflict and the weakening of the economy accelerated outmigration from Aceh. The peace deal and massive recovery programs after the 2004 tsunami led to an increase in population growth in Aceh. The Population Census 2010 confirms the role of migration in explaining Aceh’s population increase. It shows that recent migration in Aceh is 0.6 percent, while in 2000 it was equal to –9.5 percent (BPS-Statistics, 2010).
2 For instance, Aceh Tengah in Table 1 includes Bener Meriah (see Figure 1), a new region that was founded in 2003.
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For Aceh in particular, the tsunami also opened a ‘window of opportunity’ to end the thirty years of military conflict between the Free Aceh Movement and the Government of Indonesia (see, for instance, Nazara and Resosudarmo, 2007; Billon and Waizenegger, 2007).3 Furthermore, the Nias Island is expected to have an opportunity to improve its local development quality and to reduce poverty through the so called ‘build back better’ approach in reconstruction and rehabilitation programs.4 This opportunity is important since although the number of fatalities in Nias is smaller than in Aceh, the physical damages caused by the earthquake are evenly spread across a large part of the Nias Island (BRR Aceh-Nias Perwakilan Nias, 2008).
3. A framework to study the impact of the twin disasters on population dynamics As already stated in the introduction, we follow an approach developed by Davis and Weinstein (2002) to empirically investigate the relevance of the locational fundamental theory in Northern Sumatera after the twin disasters. There are three theoretical strands that have been used in studying the impact of shocks on the distribution of cities, viz. increasing returns to scale, random growth, and locational fundamentals. According to Bosker et al. (2008), all three theoretical strands predict a stable city size distribution in equilibrium, but they yield a quite different reaction to shocks. With increasing returns, a large shock has the potential to (radically) change the city size distribution. Random growth theory predicts a permanent effect of shocks on city sizes and on the relative position of cities within the distribution. Finally, the locational fundamental theory argues that a large shock has a temporary effect only on relative city size as far as the shock does not change the underlying locational fundamentals themselves (i.e., the first nature geography). Regarding the findings in Davis and Weinstein (2002) that the Japanese case supports the third theory, Polèse and Dennis-Jacob (2010) find that actually the characteristics of a
3 On December 28, 2006, two days after the tsunami, Aceh was opened to outsiders. To be more precise, the agreement of peace (the Helsinki Memorandum of Understanding) was officially signed on August 25, 2005, by Hamid Awaluddin (chief Indonesian negotiator) and Malik Mahmud (leader of the Free Aceh Movement) after a series of negotiations. These negotiations were moderated by former Finnish President Martti Ahtisaari. In 2008, Ahtisaari received the Nobel Peace Prize for his significant efforts over more than three decades to resolve international conflicts. 4 ‘Build back better’ or ‘building back better’ was the tag line or mantra which many groups gave to the post- disaster reconstruction. Its ambition is to do more than just restore Aceh and Nias to their previous conditions. See, for instance, Kennedy et al. (2008).
7 country may determine which theory is most applicable. For instance, there is a difference between countries with mature urban systems and developing nations in the probability that the top city will be pushed aside by an upstart. This probability is rather small in mature urban systems, but it is more likely to materialize in developing nations. They also noted that ‘fundamental advantages’ are in part a function of technology and preferences: the manifestation of ‘fundamental’ was different in the past (for instance, ports were fundamental for transoceanic travel) than it is these days (for instance, part of travel through ports having been replaced by air travel). In short, based on their study on the dynamic changes of the top cities in national urban hierarchies, they concluded that political events and technological change can disturb the ‘fundamental’ advantages of the first big cities. When the locational fundamental theory applies in the context of Northern Sumatera, we should not find permanent changes in the rank of affected regions nor of non-affected regions after the twin disasters. However, it is reasonable to expect that there are rank changes in the aftermath of disasters and that it takes years before they are back to their pre-disaster rank. In checking the expectations, it is useful to compare the regions’ rank after the disaster (viz. in 2005 and 2010) with the 2003 rank along with the computation of simple correlations between rank and size in pre- and post-disaster periods. This can then be followed by relating rank and size of population by using the Zipf equation as the standard procedure. Regarding this Zipf equation, Gabaix and Ibragimov (2009) noted that for small samples, the estimated power law coefficient in the classic OLS Zipf regression is biased and inefficient. The proposed procedure to solve this problem is to shift the rank by 0.5. Then, the OLS model for the Zipf equation to be estimated is:
log(Ri – 0.5) = α – β·log(Pi) + εi , (1)
where R is the rank of region i in the system of regions (based on population), Pi is the population of region i, and β is the power component that is our main coefficient of interest. Following Gabaix and Ibragimov (2009), the standard error of the estimated power component (b) is computed as [√(2/N)]·b.
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3.1. The spatial aspect of disaster on population growth The description of the change of rank of regions will provide a useful first indication whether the tsunami and earthquake had permanent impacts on population dynamics in these regions. The main concern here is to find out the impact of disasters on population growth by adopting an empirical model that has been used in Davis and Weinstein (2002) and Brakman et al. (2004). The basic model in exploring whether a disaster has a permanent or temporary impact on the population growth is as follows: